Big Data Approaches in Heart Failure Research

Current Heart Failure Reports, vol. 17, pp. 213–224

Abstract

PURPOSE OF REVIEW: The goal of this review is to summarize the state of big data analyses in the study of heart failure (HF). We discuss the use of big data in the HF space, focusing on "omics" and clinical data. We address some limitations of this data, as well as their future potential. RECENT FINDINGS: Omics are providing insight into plasmal and myocardial molecular profiles in HF patients. The introduction of single cell and spatial technologies is a major advance that will reshape our understanding of cell heterogeneity and function as well as tissue architecture. Clinical data analysis focuses on HF phenotyping and prognostic modeling. Big data approaches are increasingly common in HF research. The use of methods designed for big data, such as machine learning, may help elucidate the biology underlying HF. However, important challenges remain in the translation of this knowledge into improvements in clinical care.

Authors 5

1. Jan D. Lanzer

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Faculty of Biosciences, Heidelberg University, Heidelberg, Germany

   Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany

   Internal Medicine II, Heidelberg University Hospital, Heidelberg, Germany

2. Florian Leuschner

   University Hospital Heidelberg · German Centre for Cardiovascular Research

   Affiliation as printed

   DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany

   Department of Cardiology, Medical University Hospital, Heidelberg, Germany

3. Rafael Kramann Aachen Department of Nephrology and Clinical Immunology

   Erasmus MC · RWTH Aachen University

   Affiliation as printed

   Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, Rotterdam, The Netherlands

   Department of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany

4. Rebecca T. Levinson

   Heidelberg University · University Hospital Heidelberg

   Affiliation as printed

   Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany

   Internal Medicine II, Heidelberg University Hospital, Heidelberg, Germany

5. Julio Sáez-Rodríguez corresponding Aachen Joint Research Centre for Computational Biomedicine (JRC-COMBINE) Faculty of Medicine

   Heidelberg University · University Hospital Heidelberg · RWTH Aachen University

   Affiliation as printed

   Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany. julio.saez@bioquant.uni-heidelberg.de

   Joint Research Centre for Computational Biomedicine (JRC-COMBINE), Faculty of Medicine, RWTH Aachen University, Aachen, Germany. julio.saez@bioquant.uni-heidelberg.de

   Institute for Computational Biomedicine, Bioquant, Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Heidelberg, Germany